Overview

Dataset statistics

Number of variables22
Number of observations284385
Missing cells2254014
Missing cells (%)36.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.2 MiB
Average record size in memory244.0 B

Variable types

Numeric12
Unsupported7
Categorical3

Warnings

danger has constant value "1.0" Constant
operation_car has constant value "18.0" Constant
operation_date has a high cardinality: 36898 distinct values High cardinality
operation_car is highly correlated with dangerHigh correlation
danger is highly correlated with operation_carHigh correlation
index_train has 284385 (100.0%) missing values Missing
danger has 259542 (91.3%) missing values Missing
loaded has 284385 (100.0%) missing values Missing
operation_train has 284385 (100.0%) missing values Missing
rod_train has 284385 (100.0%) missing values Missing
ssp_station_esr has 284385 (100.0%) missing values Missing
ssp_station_id has 284385 (100.0%) missing values Missing
weight_brutto has 284385 (100.0%) missing values Missing
adm is highly skewed (γ1 = 45.65797459) Skewed
df_index has unique values Unique
index_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
loaded is an unsupported type, check if it needs cleaning or further analysis Unsupported
operation_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
rod_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_esr is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
weight_brutto is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver has 2937 (1.0%) zeros Zeros
sender has 40034 (14.1%) zeros Zeros

Reproduction

Analysis started2021-04-16 09:39:41.506588
Analysis finished2021-04-16 09:40:45.875426
Duration1 minute and 4.37 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct284385
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2210794.046
Minimum1
Maximum4189899
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:46.333431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile219129.2
Q11175631
median2282291
Q33262090
95-th percentile3997662.8
Maximum4189899
Range4189898
Interquartile range (IQR)2086459

Descriptive statistics

Standard deviation1209242.33
Coefficient of variation (CV)0.5469719499
Kurtosis-1.206029417
Mean2210794.046
Median Absolute Deviation (MAD)1046187
Skewness-0.1414585436
Sum6.287166647 × 1011
Variance1.462267013 × 1012
MonotocityStrictly increasing
2021-04-16T15:40:46.601429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31477771
 
< 0.1%
8596571
 
< 0.1%
35323101
 
< 0.1%
28467361
 
< 0.1%
40606921
 
< 0.1%
14269621
 
< 0.1%
3845291
 
< 0.1%
18346471
 
< 0.1%
29772871
 
< 0.1%
33540891
 
< 0.1%
Other values (284375)284375
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
141
< 0.1%
231
< 0.1%
281
< 0.1%
401
< 0.1%
601
< 0.1%
821
< 0.1%
971
< 0.1%
991
< 0.1%
1081
< 0.1%
ValueCountFrequency (%)
41898991
< 0.1%
41898821
< 0.1%
41898581
< 0.1%
41898551
< 0.1%
41898381
< 0.1%
41898141
< 0.1%
41897641
< 0.1%
41897561
< 0.1%
41897451
< 0.1%
41897171
< 0.1%

index_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

length
Real number (ℝ≥0)

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.023860049
Minimum0.78
Maximum2.13
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:46.826425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile0.85
Q11
median1
Q31
95-th percentile1.36
Maximum2.13
Range1.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1670497071
Coefficient of variation (CV)0.1631567784
Kurtosis12.97238546
Mean1.023860049
Median Absolute Deviation (MAD)0
Skewness3.299744712
Sum291170.44
Variance0.02790560464
MonotocityNot monotonic
2021-04-16T15:40:47.099423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1172511
60.7%
1.0641746
 
14.7%
0.8727989
 
9.8%
0.8310560
 
3.7%
0.853905
 
1.4%
1.822938
 
1.0%
1.362771
 
1.0%
1.222683
 
0.9%
1.412406
 
0.8%
1.852075
 
0.7%
Other values (61)14801
 
5.2%
ValueCountFrequency (%)
0.786
 
< 0.1%
0.791940
 
0.7%
0.812
 
< 0.1%
0.829
 
< 0.1%
0.8310560
 
3.7%
0.853905
 
1.4%
0.861149
 
0.4%
0.8727989
9.8%
0.886
 
< 0.1%
0.9181
 
0.1%
ValueCountFrequency (%)
2.1318
 
< 0.1%
1.934
 
< 0.1%
1.92188
 
0.1%
1.89222
 
0.1%
1.852075
0.7%
1.8432
 
< 0.1%
1.831025
 
0.4%
1.822938
1.0%
1.7818
 
< 0.1%
1.77483
 
0.2%

car_number
Real number (ℝ≥0)

Distinct198464
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58382707.98
Minimum21094370
Maximum98099997
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:47.448423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum21094370
5-th percentile30869847.4
Q152871183
median57919664
Q363016737
95-th percentile92662030
Maximum98099997
Range77005627
Interquartile range (IQR)10145554

Descriptive statistics

Standard deviation13957131.37
Coefficient of variation (CV)0.2390627611
Kurtosis1.593582805
Mean58382707.98
Median Absolute Deviation (MAD)5072216
Skewness0.6581232365
Sum1.660316641 × 1013
Variance1.948015161 × 1014
MonotocityNot monotonic
2021-04-16T15:40:47.673423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4460666335
 
< 0.1%
9263040931
 
< 0.1%
4433501631
 
< 0.1%
3784703530
 
< 0.1%
2448246530
 
< 0.1%
3784699530
 
< 0.1%
4443927128
 
< 0.1%
4440936525
 
< 0.1%
3781234425
 
< 0.1%
3768847025
 
< 0.1%
Other values (198454)284095
99.9%
ValueCountFrequency (%)
210943701
 
< 0.1%
211361635
< 0.1%
211364293
< 0.1%
211364451
 
< 0.1%
211384741
 
< 0.1%
211391751
 
< 0.1%
212266341
 
< 0.1%
212328631
 
< 0.1%
212797081
 
< 0.1%
217767602
 
< 0.1%
ValueCountFrequency (%)
980999971
< 0.1%
980999891
< 0.1%
980999711
< 0.1%
980999631
< 0.1%
980999551
< 0.1%
980999481
< 0.1%
980999301
< 0.1%
980999221
< 0.1%
980999141
< 0.1%
980999061
< 0.1%

destination_esr
Real number (ℝ≥0)

Distinct1087
Distinct (%)0.4%
Missing1025
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean886024.297
Minimum13000
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:47.907422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum13000
5-th percentile831504
Q1863007
median887904
Q3932207
95-th percentile980802
Maximum998100
Range985100
Interquartile range (IQR)69200

Descriptive statistics

Standard deviation103294.375
Coefficient of variation (CV)0.1165818763
Kurtosis40.86339312
Mean886024.297
Median Absolute Deviation (MAD)26498
Skewness-5.741912293
Sum2.510638448 × 1011
Variance1.066972791 × 1010
MonotocityNot monotonic
2021-04-16T15:40:48.121426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86210815546
 
5.5%
86490213940
 
4.9%
86140613491
 
4.7%
8648098940
 
3.1%
9116058387
 
2.9%
8630078112
 
2.9%
9468017998
 
2.8%
8622017491
 
2.6%
8931064987
 
1.8%
8876034848
 
1.7%
Other values (1077)189620
66.7%
ValueCountFrequency (%)
130001
 
< 0.1%
149061
 
< 0.1%
157011
 
< 0.1%
160095
 
< 0.1%
18502398
0.1%
207062
 
< 0.1%
214017
 
< 0.1%
278024
 
< 0.1%
300062
 
< 0.1%
318082
 
< 0.1%
ValueCountFrequency (%)
9981008
 
< 0.1%
99750219
 
< 0.1%
9971081
 
< 0.1%
996904151
0.1%
9968001
 
< 0.1%
99660324
 
< 0.1%
99630228
 
< 0.1%
9958081
 
< 0.1%
995507118
< 0.1%
99540367
< 0.1%

adm
Real number (ℝ≥0)

SKEWED

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.06934262
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:48.273422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile20
Maximum99
Range79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8738641046
Coefficient of variation (CV)0.04354223859
Kurtosis3783.390195
Mean20.06934262
Median Absolute Deviation (MAD)0
Skewness45.65797459
Sum5707420
Variance0.7636384733
MonotocityNot monotonic
2021-04-16T15:40:48.391459image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20281254
98.9%
261881
 
0.7%
27920
 
0.3%
21253
 
0.1%
2524
 
< 0.1%
3320
 
< 0.1%
9916
 
< 0.1%
248
 
< 0.1%
226
 
< 0.1%
282
 
< 0.1%
ValueCountFrequency (%)
20281254
98.9%
21253
 
0.1%
226
 
< 0.1%
248
 
< 0.1%
2524
 
< 0.1%
261881
 
0.7%
27920
 
0.3%
282
 
< 0.1%
3320
 
< 0.1%
571
 
< 0.1%
ValueCountFrequency (%)
9916
 
< 0.1%
571
 
< 0.1%
3320
 
< 0.1%
282
 
< 0.1%
27920
0.3%
261881
0.7%
2524
 
< 0.1%
248
 
< 0.1%
226
 
< 0.1%
21253
 
0.1%

danger
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing259542
Missing (%)91.3%
Memory size11.3 MiB
1.0
24843 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74529
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.024843
 
8.7%
(Missing)259542
91.3%
2021-04-16T15:40:48.647458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:40:48.723428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1.024843
100.0%

Most occurring characters

ValueCountFrequency (%)
124843
33.3%
.24843
33.3%
024843
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number49686
66.7%
Other Punctuation24843
33.3%

Most frequent character per category

ValueCountFrequency (%)
124843
50.0%
024843
50.0%
ValueCountFrequency (%)
.24843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74529
100.0%

Most frequent character per script

ValueCountFrequency (%)
124843
33.3%
.24843
33.3%
024843
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII74529
100.0%

Most frequent character per block

ValueCountFrequency (%)
124843
33.3%
.24843
33.3%
024843
33.3%

gruz
Real number (ℝ≥0)

Distinct217
Distinct (%)0.1%
Missing894
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean392403.7683
Minimum3009
Maximum731062
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:48.824471image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3009
5-th percentile161128
Q1421034
median421034
Q3421034
95-th percentile421195
Maximum731062
Range728053
Interquartile range (IQR)0

Descriptive statistics

Standard deviation76212.44065
Coefficient of variation (CV)0.1942194413
Kurtosis4.512670237
Mean392403.7683
Median Absolute Deviation (MAD)0
Skewness-2.405033588
Sum1.112429367 × 1011
Variance5808336110
MonotocityNot monotonic
2021-04-16T15:40:48.989461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421034220656
77.6%
42119517753
 
6.2%
1611286358
 
2.2%
2360385714
 
2.0%
1610434129
 
1.5%
3210673702
 
1.3%
3210292965
 
1.0%
1611852555
 
0.9%
4212082232
 
0.8%
4210872164
 
0.8%
Other values (207)15263
 
5.4%
ValueCountFrequency (%)
300930
< 0.1%
1100555
< 0.1%
140034
 
< 0.1%
1802317
 
< 0.1%
181121
 
< 0.1%
2108316
 
< 0.1%
411191
 
< 0.1%
810465
 
< 0.1%
811353
 
< 0.1%
811451
 
< 0.1%
ValueCountFrequency (%)
7310621
 
< 0.1%
72550210
 
< 0.1%
7214847
 
< 0.1%
7114591
 
< 0.1%
7110352
 
< 0.1%
69322747
< 0.1%
6931762
 
< 0.1%
69316149
< 0.1%
6931571
 
< 0.1%
69308723
< 0.1%

loaded
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

operation_car
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
18.0
284385 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1137540
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.0
2nd row18.0
3rd row18.0
4th row18.0
5th row18.0
ValueCountFrequency (%)
18.0284385
100.0%
2021-04-16T15:40:49.281467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T15:40:49.381474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
18.0284385
100.0%

Most occurring characters

ValueCountFrequency (%)
1284385
25.0%
8284385
25.0%
.284385
25.0%
0284385
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number853155
75.0%
Other Punctuation284385
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
1284385
33.3%
8284385
33.3%
0284385
33.3%
ValueCountFrequency (%)
.284385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1137540
100.0%

Most frequent character per script

ValueCountFrequency (%)
1284385
25.0%
8284385
25.0%
.284385
25.0%
0284385
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1137540
100.0%

Most frequent character per block

ValueCountFrequency (%)
1284385
25.0%
8284385
25.0%
.284385
25.0%
0284385
25.0%

operation_date
Categorical

HIGH CARDINALITY

Distinct36898
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size20.6 MiB
2020-07-22 13:29:00
 
133
2020-07-25 07:33:00
 
130
2020-07-22 08:35:00
 
122
2020-07-30 13:50:00
 
122
2020-07-20 12:04:00
 
121
Other values (36893)
283757 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters5403315
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4080 ?
Unique (%)1.4%

Sample

1st row2020-07-16 14:10:00
2nd row2020-07-16 08:38:00
3rd row2020-07-16 05:23:00
4th row2020-07-16 08:59:00
5th row2020-07-15 21:52:00
ValueCountFrequency (%)
2020-07-22 13:29:00133
 
< 0.1%
2020-07-25 07:33:00130
 
< 0.1%
2020-07-22 08:35:00122
 
< 0.1%
2020-07-30 13:50:00122
 
< 0.1%
2020-07-20 12:04:00121
 
< 0.1%
2020-07-17 23:40:00118
 
< 0.1%
2020-07-23 04:51:00118
 
< 0.1%
2020-07-22 08:29:00115
 
< 0.1%
2020-07-22 08:34:00114
 
< 0.1%
2020-07-30 17:32:00114
 
< 0.1%
Other values (36888)283178
99.6%
2021-04-16T15:40:49.775423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-07-1715082
 
2.7%
2020-07-2214977
 
2.6%
2020-07-2614457
 
2.5%
2020-07-2814116
 
2.5%
2020-07-2913868
 
2.4%
2020-07-1613789
 
2.4%
2020-07-1813707
 
2.4%
2020-07-1913481
 
2.4%
2020-07-2713370
 
2.4%
2020-07-2513076
 
2.3%
Other values (1461)428847
75.4%

Most occurring characters

ValueCountFrequency (%)
01733004
32.1%
2857687
15.9%
-568770
 
10.5%
:568770
 
10.5%
7372421
 
6.9%
1355420
 
6.6%
284385
 
5.3%
3144005
 
2.7%
5130970
 
2.4%
4123395
 
2.3%
Other values (3)264488
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3981390
73.7%
Dash Punctuation568770
 
10.5%
Other Punctuation568770
 
10.5%
Space Separator284385
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
01733004
43.5%
2857687
21.5%
7372421
 
9.4%
1355420
 
8.9%
3144005
 
3.6%
5130970
 
3.3%
4123395
 
3.1%
988612
 
2.2%
888035
 
2.2%
687841
 
2.2%
ValueCountFrequency (%)
-568770
100.0%
ValueCountFrequency (%)
284385
100.0%
ValueCountFrequency (%)
:568770
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5403315
100.0%

Most frequent character per script

ValueCountFrequency (%)
01733004
32.1%
2857687
15.9%
-568770
 
10.5%
:568770
 
10.5%
7372421
 
6.9%
1355420
 
6.6%
284385
 
5.3%
3144005
 
2.7%
5130970
 
2.4%
4123395
 
2.3%
Other values (3)264488
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5403315
100.0%

Most frequent character per block

ValueCountFrequency (%)
01733004
32.1%
2857687
15.9%
-568770
 
10.5%
:568770
 
10.5%
7372421
 
6.9%
1355420
 
6.6%
284385
 
5.3%
3144005
 
2.7%
5130970
 
2.4%
4123395
 
2.3%
Other values (3)264488
 
4.9%

operation_st_esr
Real number (ℝ≥0)

Distinct636
Distinct (%)0.2%
Missing37
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean933430.6451
Minimum830003
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:49.929467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum830003
5-th percentile841604
Q1888803
median947005
Q3984700
95-th percentile989205
Maximum998100
Range168097
Interquartile range (IQR)95897

Descriptive statistics

Standard deviation49976.26988
Coefficient of variation (CV)0.05354042118
Kurtosis-1.054978203
Mean933430.6451
Median Absolute Deviation (MAD)39098
Skewness-0.5320380603
Sum2.654191371 × 1011
Variance2497627551
MonotocityNot monotonic
2021-04-16T15:40:50.134425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98610334368
 
12.1%
94700521138
 
7.4%
98930910977
 
3.9%
9847009271
 
3.3%
9878017024
 
2.5%
9857025985
 
2.1%
9369034831
 
1.7%
9802004571
 
1.6%
9322074455
 
1.6%
9678084267
 
1.5%
Other values (626)177461
62.4%
ValueCountFrequency (%)
830003700
0.2%
8301074
 
< 0.1%
8302001281
0.5%
83030458
 
< 0.1%
830709265
 
0.1%
831203494
 
0.2%
831400361
 
0.1%
8315041323
0.5%
83160877
 
< 0.1%
831805128
 
< 0.1%
ValueCountFrequency (%)
99810015
 
< 0.1%
99750264
< 0.1%
99710810
 
< 0.1%
99690414
 
< 0.1%
9968001
 
< 0.1%
99660323
 
< 0.1%
99630243
 
< 0.1%
99580826
 
< 0.1%
995507147
0.1%
9954032
 
< 0.1%

operation_st_id
Real number (ℝ≥0)

Distinct636
Distinct (%)0.2%
Missing37
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2001030976
Minimum2000035090
Maximum2002030163
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:50.327465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2000035090
5-th percentile2000035688
Q12000037498
median2001930528
Q32002025275
95-th percentile2002025661
Maximum2002030163
Range1995073
Interquartile range (IQR)1987777

Descriptive statistics

Standard deviation975870.1466
Coefficient of variation (CV)0.0004876836783
Kurtosis-1.994430623
Mean2001030976
Median Absolute Deviation (MAD)96081
Skewness-0.03362778002
Sum5.689891558 × 1014
Variance9.52322543 × 1011
MonotocityNot monotonic
2021-04-16T15:40:50.524460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200202566134368
 
12.1%
200202527521138
 
7.4%
200003913210977
 
3.9%
20020256519271
 
3.3%
20020256837024
 
2.5%
20020256575985
 
2.1%
20000374984831
 
1.7%
20020256074571
 
1.6%
20000370644455
 
1.6%
20000386044267
 
1.5%
Other values (626)177461
62.4%
ValueCountFrequency (%)
200003509011
 
< 0.1%
2000035110235
0.1%
2000035130304
0.1%
2000035140149
 
0.1%
2000035162516
0.2%
20000351763
 
< 0.1%
2000035182325
0.1%
2000035194135
 
< 0.1%
200003521218
 
< 0.1%
20000352227
 
< 0.1%
ValueCountFrequency (%)
20020301631
 
< 0.1%
200203016127
 
< 0.1%
20020301591475
 
0.5%
200203015711
 
< 0.1%
20020266092645
 
0.9%
20020266071
 
< 0.1%
20020256837024
2.5%
2002025669263
 
0.1%
2002025667120
 
< 0.1%
200202566537
 
< 0.1%

operation_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

receiver
Real number (ℝ≥0)

ZEROS

Distinct2104
Distinct (%)0.7%
Missing892
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean41214524.29
Minimum0
Maximum99964849
Zeros2937
Zeros (%)1.0%
Memory size2.2 MiB
2021-04-16T15:40:50.722465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile161878
Q110891709
median26648903
Q377096306
95-th percentile93294342
Maximum99964849
Range99964849
Interquartile range (IQR)66204597

Descriptive statistics

Standard deviation33669967.5
Coefficient of variation (CV)0.8169442224
Kurtosis-1.436794945
Mean41214524.29
Median Absolute Deviation (MAD)25589939
Skewness0.2902644559
Sum1.168402913 × 1013
Variance1.133666711 × 1015
MonotocityNot monotonic
2021-04-16T15:40:50.895468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8029885813704
 
4.8%
2043996213333
 
4.7%
2663568712315
 
4.3%
9314985811640
 
4.1%
4813418710356
 
3.6%
108917098902
 
3.1%
1618788378
 
2.9%
266440967316
 
2.6%
149993555982
 
2.1%
944213865713
 
2.0%
Other values (2094)185854
65.4%
ValueCountFrequency (%)
02937
1.0%
185954
 
< 0.1%
44474812
 
0.3%
5962510
 
< 0.1%
6453753
 
< 0.1%
832623790
1.3%
10514729
 
< 0.1%
1052133
 
< 0.1%
1054574
 
< 0.1%
10615822
 
< 0.1%
ValueCountFrequency (%)
999648492
 
< 0.1%
998637238
 
< 0.1%
9984925556
 
< 0.1%
994351579
 
< 0.1%
994175151
 
< 0.1%
99415491306
0.1%
993390281
 
< 0.1%
990299602
 
< 0.1%
9894755319
 
< 0.1%
989336861
 
< 0.1%

rodvag
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.14930816
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size2.2 MiB
2021-04-16T15:40:51.036464image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q160
median60
Q370
95-th percentile92
Maximum99
Range79
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.21934155
Coefficient of variation (CV)0.2568411599
Kurtosis0.7669385446
Mean63.14930816
Median Absolute Deviation (MAD)0
Skewness0.05993310741
Sum17958716
Variance263.0670404
MonotocityNot monotonic
2021-04-16T15:40:51.169469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
60172235
60.6%
9031606
 
11.1%
4027083
 
9.5%
7027060
 
9.5%
9610589
 
3.7%
209356
 
3.3%
922468
 
0.9%
931850
 
0.7%
951715
 
0.6%
87418
 
0.1%
ValueCountFrequency (%)
209356
 
3.3%
4027083
 
9.5%
60172235
60.6%
7027060
 
9.5%
87418
 
0.1%
9031606
 
11.1%
922468
 
0.9%
931850
 
0.7%
951715
 
0.6%
9610589
 
3.7%
ValueCountFrequency (%)
995
 
< 0.1%
9610589
 
3.7%
951715
 
0.6%
931850
 
0.7%
922468
 
0.9%
9031606
 
11.1%
87418
 
0.1%
7027060
 
9.5%
60172235
60.6%
4027083
 
9.5%

rod_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

sender
Real number (ℝ≥0)

ZEROS

Distinct706
Distinct (%)0.2%
Missing892
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean44587853.56
Minimum0
Maximum99976605
Zeros40034
Zeros (%)14.1%
Memory size2.2 MiB
2021-04-16T15:40:51.331467image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14733886
median52682351
Q369546824
95-th percentile93315475
Maximum99976605
Range99976605
Interquartile range (IQR)64812938

Descriptive statistics

Standard deviation34019457.29
Coefficient of variation (CV)0.7629758908
Kurtosis-1.439870612
Mean44587853.56
Median Absolute Deviation (MAD)34534395
Skewness-0.07445773709
Sum1.264034437 × 1013
Variance1.157323474 × 1015
MonotocityNot monotonic
2021-04-16T15:40:51.504463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040034
 
14.1%
5268235123725
 
8.3%
6839852822503
 
7.9%
9331547518358
 
6.5%
6954682417767
 
6.2%
5673865716696
 
5.9%
8326210171
 
3.6%
283848648572
 
3.0%
151994508128
 
2.9%
932943427825
 
2.8%
Other values (696)109714
38.6%
ValueCountFrequency (%)
040034
14.1%
6453730
 
< 0.1%
8326210171
 
3.6%
1189771074
 
0.4%
1487253
 
< 0.1%
15379018
 
< 0.1%
16020665
 
< 0.1%
1618781
 
< 0.1%
186476138
 
< 0.1%
1867202343
 
0.8%
ValueCountFrequency (%)
999766051
 
< 0.1%
998637232
 
< 0.1%
9984925511
 
< 0.1%
997695854
 
< 0.1%
99415491146
0.1%
9902996022
 
< 0.1%
989512821
 
< 0.1%
989439921
 
< 0.1%
984045455
 
< 0.1%
981149651
 
< 0.1%

ssp_station_esr
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

ssp_station_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

tare_weight
Real number (ℝ≥0)

Distinct306
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242.4687202
Minimum0
Maximum890
Zeros1
Zeros (%)< 0.1%
Memory size2.2 MiB
2021-04-16T15:40:51.699424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile210
Q1233
median240
Q3247
95-th percentile270
Maximum890
Range890
Interquartile range (IQR)14

Descriptive statistics

Standard deviation33.00265689
Coefficient of variation (CV)0.1361109872
Kurtosis50.0745029
Mean242.4687202
Median Absolute Deviation (MAD)7
Skewness5.682295411
Sum68954467
Variance1089.175361
MonotocityNot monotonic
2021-04-16T15:40:51.870423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24528572
 
10.0%
24028136
 
9.9%
23511316
 
4.0%
2379473
 
3.3%
2479074
 
3.2%
2388918
 
3.1%
1848214
 
2.9%
2368173
 
2.9%
2338160
 
2.9%
2438148
 
2.9%
Other values (296)156201
54.9%
ValueCountFrequency (%)
01
 
< 0.1%
1721
 
< 0.1%
1731
 
< 0.1%
1781
 
< 0.1%
17921
 
< 0.1%
18014
 
< 0.1%
18119
 
< 0.1%
1825
 
< 0.1%
1835
 
< 0.1%
1848214
2.9%
ValueCountFrequency (%)
8904
 
< 0.1%
8801
 
< 0.1%
69044
< 0.1%
6773
 
< 0.1%
6635
 
< 0.1%
6597
 
< 0.1%
64113
 
< 0.1%
6407
 
< 0.1%
6393
 
< 0.1%
6296
 
< 0.1%

weight_brutto
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing284385
Missing (%)100.0%
Memory size2.2 MiB

Interactions

2021-04-16T15:40:02.461509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:02.707475image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:03.057511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:03.311477image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:03.648473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:04.017476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:04.323509image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:04.589512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:04.837129image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:05.084136image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:05.328132image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:05.556166image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:05.794130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:06.065168image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:06.298167image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:06.553130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:06.807131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:07.071167image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:07.340133image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:07.582663image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:07.842087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:08.089084image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:08.328086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:08.695050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:08.975051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:09.225048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:09.488083image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:09.754084image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:10.037052image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:10.338048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:10.593925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:10.869596image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:11.129561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:11.387601image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:11.634229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:11.891196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:12.130229image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:12.381939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:12.642803image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:12.909852image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:13.178816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:13.421818image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:13.684821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:14.024814image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:14.375812image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:14.669816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:14.927816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:15.198849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:15.453812image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:15.717850image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:15.985813image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:16.260686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:16.621971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:16.884560image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:17.132014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:17.389014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:17.644014image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:17.900769image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:18.168815image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:18.408771image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:18.679747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:18.954782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:19.223778image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:19.473780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:19.740945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:19.998945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:20.246502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:20.501298image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:20.765776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:21.041934image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:21.284068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:21.547747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:21.820753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:22.098753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:22.355753image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:22.625747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:22.896751image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:23.161752image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:23.446747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:23.712750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:24.000746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:24.290747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:24.700748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:25.074751image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:25.402354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:25.681389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:25.960385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:26.382353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:26.715349image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:26.985353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:27.228385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:27.553173image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:27.907207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:28.249174image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:28.594170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:28.879188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:29.194192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:29.482224image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:29.914192image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:30.276187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:30.527191image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:30.786224image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:31.106191image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:31.341224image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:31.597228image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:32.115188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:32.386186image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:32.669190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:32.946188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:33.269185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:33.515017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:33.751465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:34.018427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:34.291427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:34.535457image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:34.893424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:35.308427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:35.637460image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:35.928422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:36.394427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:36.688427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:37.070422image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:37.379428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:37.649461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:37.984421image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:38.238423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:38.594427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:39.008424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:39.714430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:40.125425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:40.531425image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T15:40:40.877426image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-16T15:40:52.280465image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-16T15:40:52.644466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-16T15:40:52.984487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-16T15:40:53.313493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-16T15:40:53.504488image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-16T15:40:41.754423image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-16T15:40:42.728463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-16T15:40:44.274429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-16T15:40:45.011424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
01NaN1.062827035862201.020.0NaN421034.0NaN18.02020-07-16 14:10:00984700.02.002026e+09NaN93149858.060.0NaN68398528.0NaNNaN249.0NaN
114NaN1.062845375913206.020.0NaN421034.0NaN18.02020-07-16 08:38:00967704.02.002026e+09NaN13141274.060.0NaN69546824.0NaNNaN245.0NaN
223NaN1.062845052911605.020.0NaN421034.0NaN18.02020-07-16 05:23:00979504.02.002027e+09NaN161878.060.0NaN68398528.0NaNNaN241.0NaN
328NaN1.062846324967808.020.0NaN421034.0NaN18.02020-07-16 08:59:00967600.02.000039e+09NaN1126163.060.0NaN69546824.0NaNNaN245.0NaN
440NaN1.062843214967808.020.0NaN421034.0NaN18.02020-07-15 21:52:00967600.02.000039e+09NaN1126163.060.0NaN69546824.0NaNNaN245.0NaN
560NaN1.062844071861406.020.0NaN421034.0NaN18.02020-07-16 06:45:00986103.02.002026e+09NaN20439962.060.0NaN69546824.0NaNNaN245.0NaN
682NaN1.062840061861406.020.0NaN421034.0NaN18.02020-07-15 22:59:00986103.02.002026e+09NaN20439962.060.0NaN69546824.0NaNNaN245.0NaN
797NaN1.062841671987801.020.0NaN161062.0NaN18.02020-07-15 21:03:00988306.02.000039e+09NaN1126648.060.0NaN0.0NaNNaN220.0NaN
899NaN1.062839121861406.020.0NaN421034.0NaN18.02020-07-16 16:42:00980200.02.002026e+09NaN20439962.060.0NaN69546824.0NaNNaN245.0NaN
9108NaN1.062838933852708.020.0NaN421034.0NaN18.02020-07-15 22:26:00864902.02.000040e+09NaN55472826.060.0NaN56738657.0NaNNaN247.0NaN

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
2843754189717NaN1.062823760862201.020.0NaN421034.0NaN18.02020-07-16 14:52:00967808.02.000039e+09NaN93149858.060.0NaN68398528.0NaNNaN250.0NaN
2843764189745NaN1.062823232862201.020.0NaN421034.0NaN18.02020-07-16 04:23:00987801.02.002026e+09NaN93149858.060.0NaN68398528.0NaNNaN244.0NaN
2843774189756NaN1.062823109862201.020.0NaN421034.0NaN18.02020-07-16 10:59:00986103.02.002026e+09NaN93149858.060.0NaN68398528.0NaNNaN245.0NaN
2843784189764NaN1.062821848873308.020.0NaN421195.0NaN18.02020-07-16 16:50:00871802.02.001934e+09NaN94174901.060.0NaN68398528.0NaNNaN248.0NaN
2843794189814NaN1.062820477862201.020.0NaN421034.0NaN18.02020-07-16 10:59:00986103.02.002026e+09NaN93149858.060.0NaN68398528.0NaNNaN246.0NaN
2843804189838NaN1.062817416862201.020.0NaN421034.0NaN18.02020-07-16 14:54:00967808.02.000039e+09NaN93149858.060.0NaN68398528.0NaNNaN249.0NaN
2843814189855NaN1.062815881862201.020.0NaN421034.0NaN18.02020-07-16 09:46:00980200.02.002026e+09NaN93149858.060.0NaN68398528.0NaNNaN245.0NaN
2843824189858NaN1.062817861862201.020.0NaN421034.0NaN18.02020-07-16 08:15:00967808.02.000039e+09NaN93149858.060.0NaN68398528.0NaNNaN250.0NaN
2843834189882NaN1.062816723862201.020.0NaN421034.0NaN18.02020-07-16 13:56:00924501.02.000037e+09NaN93149858.060.0NaN68398528.0NaNNaN244.0NaN
2843844189899NaN1.062814017862201.020.0NaN421034.0NaN18.02020-07-16 04:04:00986103.02.002026e+09NaN93149858.060.0NaN68398528.0NaNNaN245.0NaN